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Program Information

Quantitative Imaging Metrology: What Should Be Assessed and How?

M Giger
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N Petrick
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N Obuchowski

P Kinahan

M Giger1*, N Petrick2*, N Obuchowski3*, P Kinahan4*, (1) University of Chicago, Chicago, IL, (2) US Food and Drug Administration, Silver Spring, MD, (3) Case Western Reserve University, Cleveland, OH, (4) University of Washington, Seattle, WA


MO-G-12A-1 Monday 4:30PM - 6:00PM Room: 12A

The first two symposia in the Quantitative Imaging Track focused on 1) the introduction of quantitative imaging (QI) challenges and opportunities, and QI efforts of agencies and organizations such as the RSNA, NCI, FDA, and NIST, and 2) the techniques, applications, and challenges of QI, with specific examples from CT, PET/CT, and MR. This third symposium in the QI Track will focus on metrology and its importance in successfully advancing the QI field. While the specific focus will be on QI, many of the concepts presented are more broadly applicable to many areas of medical physics research and applications. As such, the topics discussed should be of interest to medical physicists involved in imaging as well as therapy. The first talk of the session will focus on the introduction to metrology and why it is critically important in QI. The second talk will focus on appropriate methods for technical performance assessment. The third talk will address statistically valid methods for algorithm comparison, a common problem not only in QI but also in other areas of medical physics. The final talk in the session will address strategies for publication of results that will allow statistically valid meta-analyses, which is critical for combining results of individual studies with typically small sample sizes in a manner that can best inform decisions and advance the field.

Learning Objectives:
1. Understand the importance of metrology in the QI efforts.
2. Understand appropriate methods for technical performance assessment.
3. Understand methods for comparing algorithms with or without reference data (i.e., “ground truth”).
4. Understand the challenges and importance of reporting results in a manner that allows for statistically valid meta-analyses.


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